Automatic gait optimization with Gaussian process regression

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 944-949

  Lizotte, Daniel ^a   Wang, Tao ^a   Bowling, Michael ^a   Schuurmans, Dale ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN APPROACHES; BIPEDAL ROBOT; GAIT-OPTIMIZATION; GAUSSIAN PROCESS REGRESSION; LOCAL FUNCTIONS; LOCAL GRADIENTS; LOCAL OPTIMIZATIONS; QUADRUPED ROBOTS;

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC);

OPTIMIZATION;

EID: 84880890296     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Sonia Chernova and Manuela Veloso. An evolutionary approach to gait learning for fourlegged robots. In Intelligent Robots and Systems, 2004.
2. G. S. Hornby, M. Fujita, S. Takamura, T. Yamamoto, and O. Hanagata. Autonomous evolution of gaits with the Sony quadruped robot. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 1297-1304, 1999.
3. G. Hornby, S. Takamura, J. Yokono, O. Hanagata, T. Yamamoto, and M. Fujita. Evolving robust gaits with AIBO. In IEEE International Conference on Robotics and Automation, pages 3040-3045, 2000.
4. M. S. Kim and W. Uther. Automatic gait optimisation for quadruped robots. In Australasian Conference on Robotics and Automation, 2003.
5. Nate Kohl and Peter Stone. Machine learning for fast quadrupedal locomotion. In The Nineteenth National Conference on Artificial Intelligence, pages 611-616, July 2004.
6. Vladimir Kolmogorov. Graph Based Algorithms for Scene Reconstruction From Two or More Views. PhD thesis, Cornell University, January 2004.
7. Jonas Mockus. Bayesian Approach to Global Optimization. Kluwer Academic Publishers, The Netherlands, 1989.
8. William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, New York, NY, USA, 1992.
9. Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
10. C. E. Rasmussen. Advanced Lectures in Machine Learning: ML Summer Schools 2003, chapter Gaussian Processes in Machine Learning. Springer-Verlag, 2004.
11. Tao Wang, Daniel Lizotte, Michael Bowling, and Dale Schuurmans. Bayesian sparse sampling for on-line reward optimization. In ICML 2005, Bonn, 2005.
12. C.Williams. Prediction with Gaussian processes. In Learning in Graphical Models. MIT, 1999.